Vital author snippets on online social networks

ABSTRACT

In one embodiment, a method includes receiving a text query from a client system of a user and parsing the text query to identify a primary entity referenced in the text query. The method also includes identifying one or more related entities for the primary entity based on one or more related-entity indexes associated with the primary entity and identifying one or more content objects matching the text query, each identified content object being associated with one or more of the related entities. The method also includes sending to the client system instructions for presenting one or more search results corresponding to one or more of the identified content objects, respectively, each search result including a reference to the associated related entity and a snippet for the related entity describing the relationship between the primary entity and the related entity.

TECHNICAL FIELD

This disclosure generally relates to social graphs and performingsearches for objects within a social-networking environment.

BACKGROUND

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. wall posts,photo-sharing, event organization, messaging, games, or advertisements)to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

Social-graph analysis views social relationships in terms of networktheory consisting of nodes and edges. Nodes represent the individualactors within the networks, and edges represent the relationshipsbetween the actors. The resulting graph-based structures are often verycomplex. There can be many types of nodes and many types of edges forconnecting nodes. In its simplest form, a social graph is a map of allof the relevant edges between all the nodes being studied.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, a user may conduct a search against theonline social network by inputting a text query into a user interface ofthe social-networking system (e.g., a query field). In response to theuser's input, the social-networking system may identify one or morecontent objects matching the inputted text query and provide one or moresearch results corresponding to one or more of the identified contentobjects for display to the user. Contextual information about the authorof a particular content object may be helpful for the querying user tojudge whether the content object is interesting, trustworthy, orreliable. The querying user may be more interested in a content objectauthored by an entity related to or knowledgeable about the topic of thecontent object than one authored by another entity. In particularembodiments, the social-networking system may specifically identify andprovide for display to the querying user search results corresponding tocontent objects from authoritative or reliable sources with respect toone or more topics of the user's text query. The social-networkingsystem may further generate and provide for display a snippet withineach search result describing a relationship between the author of thecontent object corresponding to the search result and one or more topicsof the text query. Particular embodiments may thereby enhance thetechnical capabilities of the social-networking system for modelingrelationships between content authors and the subject matter of thecontent they create, identifying and providing to a user high-qualitycontent authored by entities knowledgeable about subject matter ofinterest to the user, and effectively communicating the relevance ofsuch content to the user.

In particular embodiments, the social-networking system may identify oneor more primary entities matching one or more n-grams of a text queryand identify one or more related entities to the identified primaryentities based on one or more related-entity indexes. An entity may betreated as related to a primary entity and included in a related-entityindex associated with the primary entity because one or morerelationships between the related entity and the primary entity make therelated entity an authoritative or reliable source of information withrespect to the primary entity. In particular embodiments, thesocial-networking system may generate a snippet for each pair of primaryentity and related entity describing the relationships between theentities and store such a snippet in a related-entity index associatedwith the primary entity. In particular embodiments, thesocial-networking system may identify content objects (e.g., posts)authored by the identified related entities that match the text query,score the identified content objects based at least in part on socialsignals associated with the content objects and their authors, andprovide for display to the querying user one or more search resultscorresponding to one or more highly-scored content objects. Each of thesearch results may comprise a snippet describing a relationship betweenthe related entity authoring the corresponding content object and aprimary entity matching the text query. The snippet may provideinformation that places its corresponding search result in context,suggests why the result is shown, and indicates the relevance,importance, or reliability of the search result.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed above.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with asocial-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example partitioning for storing objects of asocial-networking system.

FIG. 4 illustrates an example related-entity index.

FIG. 5 illustrates an example user interface displaying example searchresults.

FIG. 6 illustrates an example method for providing search resultscorresponding to content objects and comprising snippets describingrelationships between authors of the content objects and entitiesreferenced by a text query.

FIG. 7 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

System Overview

FIG. 1 illustrates an example network environment 100 associated with asocial-networking system. Network environment 100 includes a clientsystem 130, a social-networking system 160, and a third-party system 170connected to each other by a network 110. Although FIG. 1 illustrates aparticular arrangement of a client system 130, a social-networkingsystem 160, a third-party system 170, and a network 110, this disclosurecontemplates any suitable arrangement of a client system 130, asocial-networking system 160, a third-party system 170, and a network110. As an example and not by way of limitation, two or more of a clientsystem 130, a social-networking system 160, and a third-party system 170may be connected to each other directly, bypassing a network 110. Asanother example, two or more of a client system 130, a social-networkingsystem 160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130,social-networking systems 160, third-party systems 170, and networks110, this disclosure contemplates any suitable number of client systems130, social-networking systems 160, third-party systems 170, andnetworks 110. As an example and not by way of limitation, networkenvironment 100 may include multiple client systems 130,social-networking systems 160, third-party systems 170, and networks110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, a social-networking system160, and a third-party system 170 to a communication network 110 or toeach other. This disclosure contemplates any suitable links 150. Inparticular embodiments, one or more links 150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOC SIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, other suitable electronicdevice, or any suitable combination thereof. This disclosurecontemplates any suitable client systems 130. A client system 130 mayenable a network user at a client system 130 to access a network 110. Aclient system 130 may enable its user to communicate with other users atother client systems 130.

In particular embodiments, a client system 130 may include a web browser132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLAFIREFOX, and may have one or more add-ons, plug-ins, or otherextensions, such as TOOLBAR or YAHOO TOOLBAR. A user at a client system130 may enter a Uniform Resource Locator (URL) or other addressdirecting a web browser 132 to a particular server (such as server 162,or a server associated with a third-party system 170), and the webbrowser 132 may generate a Hyper Text Transfer Protocol (HTTP) requestand communicate the HTTP request to server. The server may accept theHTTP request and communicate to a client system 130 one or more HyperText Markup Language (HTML) files responsive to the HTTP request. Theclient system 130 may render a web interface (e.g. a webpage) based onthe HTML files from the server for presentation to the user. Thisdisclosure contemplates any suitable source files. As an example and notby way of limitation, a web interface may be rendered from HTML files,Extensible Hyper Text Markup Language (XHTML) files, or ExtensibleMarkup Language (XML) files, according to particular needs. Suchinterfaces may also execute scripts such as, for example and withoutlimitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT,combinations of markup language and scripts such as AJAX (AsynchronousJAVASCRIPT and XML), and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Social Graphs

FIG. 2 illustrates an example social graph 200. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 200 in one or more data stores. In particular embodiments,the social graph 200 may include multiple nodes—which may includemultiple user nodes 202 or multiple concept nodes 204—and multiple edges206 connecting the nodes. The example social graph 200 illustrated inFIG. 2 is shown, for didactic purposes, in a two-dimensional visual maprepresentation. In particular embodiments, a social-networking system160, a client system 130, or a third-party system 170 may access thesocial graph 200 and related social-graph information for suitableapplications. The nodes and edges of the social graph 200 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 200.

In particular embodiments, a user node 202 may correspond to a user ofthe social-networking system 160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or overthe social-networking system 160. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 202 corresponding tothe user, and store the user node 202 in one or more data stores. Usersand user nodes 202 described herein may, where appropriate, refer toregistered users and user nodes 202 associated with registered users. Inaddition or as an alternative, users and user nodes 202 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 202may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 202 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 202 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node204 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160. As an example and not by way oflimitation, information of a concept may include a name or a title; oneor more images (e.g., an image of the cover page of a book); a location(e.g., an address or a geographical location); a website (which may beassociated with a URL); contact information (e.g., a phone number or anemail address); other suitable concept information; or any suitablecombination of such information. In particular embodiments, a conceptnode 204 may be associated with one or more data objects correspondingto information associated with concept node 204. In particularembodiments, a concept node 204 may correspond to one or more webinterfaces.

In particular embodiments, a node in the social graph 200 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160. Profile interfaces may also behosted on third-party websites associated with a third-party system 170.As an example and not by way of limitation, a profile interfacecorresponding to a particular external web interface may be theparticular external web interface and the profile interface maycorrespond to a particular concept node 204. Profile interfaces may beviewable by all or a selected subset of other users. As an example andnot by way of limitation, a user node 202 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 204 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 204.

In particular embodiments, a concept node 204 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 202 corresponding to the user and a conceptnode 204 corresponding to the third-party web interface or resource andstore edge 206 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 200 maybe connected to each other by one or more edges 206. An edge 206connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 206 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 206 connecting the first user's user node 202 to thesecond user's user node 202 in the social graph 200 and store edge 206as social-graph information in one or more of data stores 164. In theexample of FIG. 2, the social graph 200 includes an edge 206 indicatinga friend relation between user nodes 202 of user “A” and user “B” and anedge indicating a friend relation between user nodes 202 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 206 with particular attributes connecting particular user nodes202, this disclosure contemplates any suitable edges 206 with anysuitable attributes connecting user nodes 202. As an example and not byway of limitation, an edge 206 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), sub scriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and aconcept node 204 may represent a particular action or activity performedby a user associated with user node 202 toward a concept associated witha concept node 204. As an example and not by way of limitation, asillustrated in FIG. 2, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 204 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (SPOTIFY, which is an onlinemusic application). In this case, the social-networking system 160 maycreate a “listened” edge 206 and a “used” edge (as illustrated in FIG.2) between user nodes 202 corresponding to the user and concept nodes204 corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 206 (asillustrated in FIG. 2) between concept nodes 204 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 206corresponds to an action performed by an external application (SPOTIFY)on an external audio file (the song “Imagine”). Although this disclosuredescribes particular edges 206 with particular attributes connectinguser nodes 202 and concept nodes 204, this disclosure contemplates anysuitable edges 206 with any suitable attributes connecting user nodes202 and concept nodes 204. Moreover, although this disclosure describesedges between a user node 202 and a concept node 204 representing asingle relationship, this disclosure contemplates edges between a usernode 202 and a concept node 204 representing one or more relationships.As an example and not by way of limitation, an edge 206 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 206 may represent each type of relationship(or multiples of a single relationship) between a user node 202 and aconcept node 204 (as illustrated in FIG. 2 between user node 202 foruser “E” and concept node 204 for “SPOTIFY”).

In particular embodiments, the social-networking system 160 may createan edge 206 between a user node 202 and a concept node 204 in the socialgraph 200. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 204 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 206 between user node202 associated with the user and concept node 204, as illustrated by“like” edge 206 between the user and concept node 204. In particularembodiments, the social-networking system 160 may store an edge 206 inone or more data stores. In particular embodiments, an edge 206 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 206 may be formed between user node 202 corresponding to thefirst user and concept nodes 204 corresponding to those concepts.Although this disclosure describes forming particular edges 206 inparticular manners, this disclosure contemplates forming any suitableedges 206 in any suitable manner.

Search Queries on Online Social Networks

In particular embodiments, the social-networking system 160 may receive,from a client system of a user of an online social network, a queryinputted by the user. The user may submit the query to thesocial-networking system 160 by, for example, selecting a query input orinputting text into query field. A user of an online social network maysearch for information relating to a specific subject matter (e.g.,users, concepts, external content or resource) by providing a shortphrase describing the subject matter, often referred to as a “searchquery,” to a search engine. The query may be an unstructured text queryand may comprise one or more text strings (which may include one or moren-grams). In general, a user may input any character string into a queryfield to search for content on the social-networking system 160 thatmatches the text query. The social-networking system 160 may then searcha data store 164 (or, in particular, a social-graph database) toidentify content matching the query. The search engine may conduct asearch based on the query phrase using various search algorithms andgenerate search results that identify resources or content (e.g.,user-profile interfaces, content-profile interfaces, or externalresources) that are most likely to be related to the search query. Toconduct a search, a user may input or send a search query to the searchengine. In response, the search engine may identify one or moreresources that are likely to be related to the search query, each ofwhich may individually be referred to as a “search result,” orcollectively be referred to as the “search results” corresponding to thesearch query. The identified content may include, for example,social-graph elements (i.e., user nodes 202, concept nodes 204, edges206), profile interfaces, external web interfaces, or any combinationthereof. The social-networking system 160 may then generate asearch-results interface with search results corresponding to theidentified content and send the search-results interface to the user.The search results may be presented to the user, often in the form of alist of links on the search-results interface, each link beingassociated with a different interface that contains some of theidentified resources or content. In particular embodiments, each link inthe search results may be in the form of a Uniform Resource Locator(URL) that specifies where the corresponding interface is located andthe mechanism for retrieving it. The social-networking system 160 maythen send the search-results interface to the web browser 132 on theuser's client system 130. The user may then click on the URL links orotherwise select the content from the search-results interface to accessthe content from the social-networking system 160 or from an externalsystem (such as, for example, a third-party system 170), as appropriate.The resources may be ranked and presented to the user according to theirrelative degrees of relevance to the search query. The search resultsmay also be ranked and presented to the user according to their relativedegree of relevance to the user. In other words, the search results maybe personalized for the querying user based on, for example,social-graph information, user information, search or browsing historyof the user, or other suitable information related to the user. Inparticular embodiments, ranking of the resources may be determined by aranking algorithm implemented by the search engine. As an example andnot by way of limitation, resources that are more relevant to the searchquery or to the user may be ranked higher than the resources that areless relevant to the search query or the user. In particularembodiments, the search engine may limit its search to resources andcontent on the online social network. However, in particularembodiments, the search engine may also search for resources or contentson other sources, such as a third-party system 170, the internet orWorld Wide Web, or other suitable sources. Although this disclosuredescribes querying the social-networking system 160 in a particularmanner, this disclosure contemplates querying the social-networkingsystem 160 in any suitable manner.

Typeahead Processes and Queries

In particular embodiments, one or more client-side and/or backend(server-side) processes may implement and utilize a “typeahead” featurethat may automatically attempt to match social-graph elements (e.g.,user nodes 202, concept nodes 204, or edges 206) to informationcurrently being entered by a user in an input form rendered inconjunction with a requested interface (such as, for example, auser-profile interface, a concept-profile interface, a search-resultsinterface, a user interface/view state of a native applicationassociated with the online social network, or another suitable interfaceof the online social network), which may be hosted by or accessible inthe social-networking system 160. In particular embodiments, as a useris entering text to make a declaration, the typeahead feature mayattempt to match the string of textual characters being entered in thedeclaration to strings of characters (e.g., names, descriptions)corresponding to users, concepts, or edges and their correspondingelements in the social graph 200. In particular embodiments, when amatch is found, the typeahead feature may automatically populate theform with a reference to the social-graph element (such as, for example,the node name/type, node ID, edge name/type, edge ID, or anothersuitable reference or identifier) of the existing social-graph element.In particular embodiments, as the user enters characters into a formbox, the typeahead process may read the string of entered textualcharacters. As each keystroke is made, the frontend-typeahead processmay send the entered character string as a request (or call) to thebackend-typeahead process executing within the social-networking system160. In particular embodiments, the typeahead process may use one ormore matching algorithms to attempt to identify matching social-graphelements. In particular embodiments, when a match or matches are found,the typeahead process may send a response to the user's client system130 that may include, for example, the names (name strings) ordescriptions of the matching social-graph elements as well as,potentially, other metadata associated with the matching social-graphelements. As an example and not by way of limitation, if a user entersthe characters “pok” into a query field, the typeahead process maydisplay a drop-down menu that displays names of matching existingprofile interfaces and respective user nodes 202 or concept nodes 204,such as a profile interface named or devoted to “poker” or “pokemon,”which the user can then click on or otherwise select thereby confirmingthe desire to declare the matched user or concept name corresponding tothe selected node.

More information on typeahead processes may be found in U.S. patentapplication Ser. No. 12/763,162, filed 19 Apr. 2010, and U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, which areincorporated by reference.

In particular embodiments, the typeahead processes described herein maybe applied to search queries entered by a user. As an example and not byway of limitation, as a user enters text characters into a query field,a typeahead process may attempt to identify one or more user nodes 202,concept nodes 204, or edges 206 that match the string of charactersentered into the query field as the user is entering the characters. Asthe typeahead process receives requests or calls including a string orn-gram from the text query, the typeahead process may perform or causeto be performed a search to identify existing social-graph elements(i.e., user nodes 202, concept nodes 204, edges 206) having respectivenames, types, categories, or other identifiers matching the enteredtext. The typeahead process may use one or more matching algorithms toattempt to identify matching nodes or edges. When a match or matches arefound, the typeahead process may send a response to the user's clientsystem 130 that may include, for example, the names (name strings) ofthe matching nodes as well as, potentially, other metadata associatedwith the matching nodes. The typeahead process may then display adrop-down menu that displays names of matching existing profileinterfaces and respective user nodes 202 or concept nodes 204, anddisplays names of matching edges 206 that may connect to the matchinguser nodes 202 or concept nodes 204, which the user can then click on orotherwise select thereby confirming the desire to search for the matcheduser or concept name corresponding to the selected node, or to searchfor users or concepts connected to the matched users or concepts by thematching edges. Alternatively, the typeahead process may simplyauto-populate the form with the name or other identifier of thetop-ranked match rather than display a drop-down menu. The user may thenconfirm the auto-populated declaration simply by keying “enter” on akeyboard or by clicking on the auto-populated declaration. Upon userconfirmation of the matching nodes and edges, the typeahead process maysend a request that informs the social-networking system 160 of theuser's confirmation of a query containing the matching social-graphelements. In response to the request sent, the social-networking system160 may automatically (or alternately based on an instruction in therequest) call or otherwise search a social-graph database for thematching social-graph elements, or for social-graph elements connectedto the matching social-graph elements as appropriate. Although thisdisclosure describes applying the typeahead processes to search queriesin a particular manner, this disclosure contemplates applying thetypeahead processes to search queries in any suitable manner.

In connection with search queries and search results, particularembodiments may utilize one or more systems, components, elements,functions, methods, operations, or steps disclosed in U.S. patentapplication Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, and U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, which areincorporated by reference.

Structured Search Queries

In particular embodiments, in response to a text query received from afirst user (i.e., the querying user), the social-networking system 160may parse the text query and identify portions of the text query thatcorrespond to particular social-graph elements. However, in some cases aquery may include one or more terms that are ambiguous, where anambiguous term is a term that may possibly correspond to multiplesocial-graph elements. To parse the ambiguous term, thesocial-networking system 160 may access a social graph 200 and thenparse the text query to identify the social-graph elements thatcorresponded to ambiguous n-grams from the text query. Thesocial-networking system 160 may then generate a set of structuredqueries, where each structured query corresponds to one of the possiblematching social-graph elements. These structured queries may be based onstrings generated by a grammar model, such that they are rendered in anatural-language syntax with references to the relevant social-graphelements. As an example and not by way of limitation, in response to thetext query, “show me friends of my girlfriend,” the social-networkingsystem 160 may generate a structured query “Friends of Stephanie,” where“Friends” and “Stephanie” in the structured query are referencescorresponding to particular social-graph elements. The reference to“Stephanie” would correspond to a particular user node 202 (where thesocial-networking system 160 has parsed the n-gram “my girlfriend” tocorrespond with a user node 202 for the user “Stephanie”), while thereference to “Friends” would correspond to friend-type edges 206connecting that user node 202 to other user nodes 202 (i.e., edges 206connecting to “Stephanie's” first-degree friends). When executing thisstructured query, the social-networking system 160 may identify one ormore user nodes 202 connected by friend-type edges 206 to the user node202 corresponding to “Stephanie”. As another example and not by way oflimitation, in response to the text query, “friends who work atfacebook,” the social-networking system 160 may generate a structuredquery “My friends who work at Facebook,” where “my friends,” “work at,”and “Facebook” in the structured query are references corresponding toparticular social-graph elements as described previously (i.e., afriend-type edge 206, a work-at-type edge 206, and concept node 204corresponding to the company “Facebook”). By providing suggestedstructured queries in response to a user's text query, thesocial-networking system 160 may provide a powerful way for users of theonline social network to search for elements represented in the socialgraph 200 based on their social-graph attributes and their relation tovarious social-graph elements. Structured queries may allow a queryinguser to search for content that is connected to particular users orconcepts in the social graph 200 by particular edge-types. Thestructured queries may be sent to the first user and displayed in adrop-down menu (via, for example, a client-side typeahead process),where the first user can then select an appropriate query to search forthe desired content. Some of the advantages of using the structuredqueries described herein include finding users of the online socialnetwork based upon limited information, bringing together virtualindexes of content from the online social network based on the relationof that content to various social-graph elements, or finding contentrelated to you and/or your friends. Although this disclosure describesgenerating particular structured queries in a particular manner, thisdisclosure contemplates generating any suitable structured queries inany suitable manner.

More information on element detection and parsing queries may be foundin U.S. patent application Ser. No. 13/556,072, filed 23 Jul. 2012, U.S.patent application Ser. No. 13/731,866, filed 31 Dec. 2012, and U.S.patent application Ser. No. 13/732,101, filed 31 Dec. 2012, each ofwhich is incorporated by reference. More information on structuredsearch queries and grammar models may be found in U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, U.S. patentapplication Ser. No. 13/674,695, filed 12 Nov. 2012, and U.S. patentapplication Ser. No. 13/731,866, filed 31 Dec. 2012, each of which isincorporated by reference.

Generating Keywords and Keyword Queries

In particular embodiments, the social-networking system 160 may providecustomized keyword completion suggestions to a querying user as the useris inputting a text string into a query field. Keyword completionsuggestions may be provided to the user in a non-structured format. Inorder to generate a keyword completion suggestion, the social-networkingsystem 160 may access multiple sources within the social-networkingsystem 160 to generate keyword completion suggestions, score the keywordcompletion suggestions from the multiple sources, and then return thekeyword completion suggestions to the user. As an example and not by wayof limitation, if a user types the query “friends stan,” then thesocial-networking system 160 may suggest, for example, “friendsstanford,” “friends stanford university,” “friends stanley,” “friendsstanley cooper,” “friends stanley kubrick,” “friends stanley cup,” and“friends stanlonski.” In this example, the social-networking system 160is suggesting the keywords which are modifications of the ambiguousn-gram “stan,” where the suggestions may be generated from a variety ofkeyword generators. The social-networking system 160 may have selectedthe keyword completion suggestions because the user is connected in someway to the suggestions. As an example and not by way of limitation, thequerying user may be connected within the social graph 200 to theconcept node 204 corresponding to Stanford University, for example bylike- or attended-type edges 206. The querying user may also have afriend named Stanley Cooper. Although this disclosure describesgenerating keyword completion suggestions in a particular manner, thisdisclosure contemplates generating keyword completion suggestions in anysuitable manner.

More information on keyword queries may be found in U.S. patentapplication Ser. No. 14/244,748, filed 3 Apr. 2014, U.S. patentapplication Ser. No. 14/470,607, filed 27 Aug. 2014, and U.S. patentapplication Ser. No. 14/561,418, filed 5 Dec. 2014, each of which isincorporated by reference.

Indexing Based on Object-Type

FIG. 3 illustrates an example partitioning for storing objects of asocial-networking system 160. A plurality of data stores 164 (which mayalso be called “verticals”) may store objects of social-networkingsystem 160. The amount of data (e.g., data for a social graph 200)stored in the data stores may be very large. As an example and not byway of limitation, a social graph used by Facebook, Inc. of Menlo Park,Calif. can have a number of nodes in the order of 10⁸, and a number ofedges in the order of 10¹⁰. Typically, a large collection of data suchas a large database may be divided into a number of partitions. As theindex for each partition of a database is smaller than the index for theoverall database, the partitioning may improve performance in accessingthe database. As the partitions may be distributed over a large numberof servers, the partitioning may also improve performance andreliability in accessing the database. Ordinarily, a database may bepartitioned by storing rows (or columns) of the database separately. Inparticular embodiments, a database maybe partitioned based onobject-types. Data objects may be stored in a plurality of partitions,each partition holding data objects of a single object-type. Inparticular embodiments, social-networking system 160 may retrieve searchresults in response to a search query by submitting the search query toa particular partition storing objects of the same object-type as thesearch query's expected results. Although this disclosure describesstoring objects in a particular manner, this disclosure contemplatesstoring objects in any suitable manner.

In particular embodiments, each object may correspond to a particularnode of a social graph 200. An edge 206 connecting the particular nodeand another node may indicate a relationship between objectscorresponding to these nodes. In addition to storing objects, aparticular data store may also store social-graph information relatingto the object. Alternatively, social-graph information about particularobjects may be stored in a different data store from the objects.Social-networking system 160 may update the search index of the datastore based on newly received objects, and relationships associated withthe received objects.

In particular embodiments, each data store 164 may be configured tostore objects of a particular one of a plurality of object-types inrespective data storage devices 340. An object-type may be, for example,a user, a photo, a post, a comment, a message, an event listing, a webinterface, an application, a location, a user-profile interface, aconcept-profile interface, a user group, an audio file, a video, anoffer/coupon, or another suitable type of object. Although thisdisclosure describes particular types of objects, this disclosurecontemplates any suitable types of objects. As an example and not by wayof limitation, a user vertical P1 illustrated in FIG. 3 may store userobjects. Each user object stored in the user vertical P1 may comprise anidentifier (e.g., a character string), a user name, and a profilepicture for a user of the online social network. Social-networkingsystem 160 may also store in the user vertical P1 information associatedwith a user object such as language, location, education, contactinformation, interests, relationship status, a list of friends/contacts,a list of family members, privacy settings, and so on. As an example andnot by way of limitation, a post vertical P2 illustrated in FIG. 3 maystore post objects. Each post object stored in the post vertical P2 maycomprise an identifier, a text string for a post posted tosocial-networking system 160. Social-networking system 160 may alsostore in the post vertical P2 information associated with a post objectsuch as a time stamp, an author, privacy settings, users who like thepost, a count of likes, comments, a count of comments, location, and soon. As an example and not by way of limitation, a photo vertical P3 maystore photo objects (or objects of other media types such as video oraudio). Each photo object stored in the photo vertical P3 may comprisean identifier and a photo. Social-networking system 160 may also storein the photo vertical P3 information associated with a photo object suchas a time stamp, an author, privacy settings, users who are tagged inthe photo, users who like the photo, comments, and so on. In particularembodiments, each data store may also be configured to store informationassociated with each stored object in data storage devices 340.

In particular embodiments, objects stored in each vertical 164 may beindexed by one or more search indices. The search indices may be hostedby respective index server 330 comprising one or more computing devices(e.g., servers). The index server 330 may update the search indicesbased on data (e.g., a photo and information associated with a photo)submitted to social-networking system 160 by users or other processes ofsocial-networking system 160 (or a third-party system). The index server330 may also update the search indices periodically (e.g., every 24hours). The index server 330 may receive a query comprising a searchterm, and access and retrieve search results from one or more searchindices corresponding to the search term. In some embodiments, avertical corresponding to a particular object-type may comprise aplurality of physical or logical partitions, each comprising respectivesearch indices.

In particular embodiments, social-networking system 160 may receive asearch query from a PHP (Hypertext Preprocessor) process 310. The PHPprocess 310 may comprise one or more computing processes hosted by oneor more servers 162 of social-networking system 160. The search querymay be a text string or a search query submitted to the PHP process by auser or another process of social-networking system 160 (or third-partysystem 170). In particular embodiments, an aggregator 320 may beconfigured to receive the search query from PHP process 310 anddistribute the search query to each vertical. The aggregator maycomprise one or more computing processes (or programs) hosted by one ormore computing devices (e.g. servers) of the social-networking system160. Particular embodiments may maintain the plurality of verticals 164as illustrated in FIG. 3. Each of the verticals 164 may be configured tostore a single type of object indexed by a search index as describedearlier. In particular embodiments, the aggregator 320 may receive asearch request. For example, the aggregator 320 may receive a searchrequest from a PHP (Hypertext Preprocessor) process 210 illustrated inFIG. 2. In particular embodiments, the search request may comprise atext string. The search request may be a structured or substantiallyunstructured text string submitted by a user via a PHP process. Thesearch request may also be structured or a substantially unstructuredtext string received from another process of the social-networkingsystem. In particular embodiments, the aggregator 320 may determine oneor more search queries based on the received search request. Inparticular embodiments, each of the search queries may have a singleobject type for its expected results (i.e., a single result-type). Inparticular embodiments, the aggregator 320 may, for each of the searchqueries, access and retrieve search query results from at least one ofthe verticals 164, wherein the at least one vertical 164 is configuredto store objects of the object type of the search query (i.e., theresult-type of the search query). In particular embodiments, theaggregator 320 may aggregate search query results of the respectivesearch queries. For example, the aggregator 320 may submit a searchquery to a particular vertical and access index server 330 of thevertical, causing index server 330 to return results for the searchquery.

More information on indexes and search queries may be found in U.S.patent application Ser. No. 13/560,212, filed 27 Jul. 2012, U.S. patentapplication Ser. No. 13/560,901, filed 27 Jul. 2012, U.S. patentapplication Ser. No. 13/723,861, filed 21 Dec. 2012, and U.S. patentapplication Ser. No. 13/870,113, filed 25 Apr. 2013, each of which isincorporated by reference.

Vital Author Snippets

In particular embodiments, the social-networking system 160 may identifyone or more related entities to a primary entity referenced in a textquery inputted by a user and provide one or more search resultsreferencing content objects authored by the identified related entities.For each of the related entities, the social-networking system 160 mayhave generated a snippet (“vital-author snippet”) describing arelationship between the related entity and the primary entity and mayprovide the snippet for display in association with content objectsauthored by the related entity. In particular embodiments, asocial-networking system 160 may comprise an enormous amount of contentdue to continual contributions from a large number (e.g., billions) ofusers with non-uniform knowledge about topics on which they createcontent. This may create particular technical challenges when queryingfor and ranking content that necessitate technical capabilities formodeling relationships between content authors and the subject matter ofthe content they create, identifying and providing to a userhigh-quality content authored by entities knowledgeable about subjectmatter of interest to the user, and effectively communicating therelevance of such content to the user. Particular embodiments disclosedherein may provide or enhance such technical capabilities. Specifically,by providing or prioritizing search results corresponding to contentfrom authoritative or reliable sources, particular embodiments mayenhance the search functionality of the social-networking system 160 interms of its ability to find and prioritize content objects that arelikely to be informative with respect to particular persons,organizations, topics, locations, or other entities associated with aninputted text query. This may save processing resources of thesocial-networking system 160 by reducing the number of search queriesneeded for obtaining content objects satisfying a user's searchingneeds. Particular embodiments may further improve user experience byallowing a user to consider author information in deciding which contentobjects to view, thus reducing the time needed for the user to graspdesired information.

In particular embodiments, a user may search against the online socialnetwork by inputting a text query into a user interface of thesocial-networking system 160. The text query may comprise one or moren-grams. The user may input the text query by, for example, typing thetext query in a query field or clicking on a trending topic provided bythe social-networking system 160. In response to the user's input, thesocial-networking system 160 may identify one or more content objectsmatching the inputted text query. The content objects may be associatedwith the online social network, a third-party system 170, or anothersuitable system. The content objects may comprise users, profileinterfaces, posts, news stories, headlines, instant messages, group/chatroom conversations, emails, advertisements, pictures, videos, audiofiles (e.g., music), other suitable objects, or any combination thereof.The social-networking system 160 may then provide one or more searchresults corresponding to one or more of the identified content objectsfor display to the user.

In particular embodiments, it may be desirable for the social-networkingsystem 160 to determine whether particular content objects are likely tobe viewed as interesting, informative, or reliable by the user andindicate the relevance of such content objects to the user. Informationabout the author of the content object may be particularly useful forconsideration by the social-networking system 160 in making suchdeterminations. In particular embodiments, the social-networking system160 may identify one or more primary entities matching one or moren-grams of the text query and identify one or more related entities tothe identified primary entities based on one or more related-entityindexes. In case the text query corresponds to a trending topic, thesocial-networking system 160 may directly use the trending topic as aprimary entity and identify related entities to the trending topic. Theprimary entities and related entities may comprise one or more of aperson, an organization, a place, a topic, or another suitable entity.An entity may be treated as related to a primary entity and included ina related-entity index associated with the primary entity because one ormore relationships between the related entity and the primary entitymake the related entity an authoritative or reliable source ofinformation with respect to the primary entity. In particularembodiments, the social-networking system 160 may generate a snippet foreach pair of primary entity and related entity describing therelationships between the entities and store such a snippet in arelated-entity index associated with the primary entity. In particularembodiments, the social-networking system 160 may identify contentobjects (e.g., posts) authored by the identified related entities thatmatch the text query, score the identified content objects based atleast in part on social signals associated with the content objects andtheir authors, and provide for display to the querying user one or moresearch results corresponding to one or more highly-scored contentobjects. Each of the search results may comprise a snippet describing arelationship between the related entity authoring the correspondingcontent object and a primary entity matching the text query. The snippetmay provide information that places its corresponding search result incontext, suggests why the result is shown, and indicates the relevance,importance, or reliability of the search result.

As an example and not by way of limitation, a user may input a textquery “oculus new headset” in a query field associated with thesocial-networking system 160 to search against the online socialnetwork. Alternatively, this text query may be automatically inputted toa search engine associated with the social-networking system 160 whenthe user clicks on a trending topic “oculus new headset” in atrending-topic module within a newsfeed interface associated with theuser. The social-networking system 160, in response, may identify aprimary entity Oculus, which matches the n-gram “oculus” of the textquery. This entity may refer to a company that engages in the design andmanufacturing of virtual reality headsets. The social-networking system160 may then access a related-entity index to identify one or morerelated entities to Oculus (e.g., Brendan Iribe, Palmer Luckey,Facebook, Mark Zuckerberg, Surreal Vision). The identified relatedentities may comprise, for example, individuals holding importantpositions related to Oculus or companies having proprietary or businessrelationships with Oculus. The related-entity index may compriseidentification information of the related entities as well as a snippetfor each of the related entities describing a relationship between therelated entity and Oculus. The related-entity index may have beencreated, for example, based on a Wikipedia page corresponding to Oculusby extracting entity names mentioned therein. It may alternatively havebeen created based on profile interfaces associated with Oculus or oneor more other entities on the online social network or social graphinformation associated with Oculus. The related entities may haveauthored content objects and published the content objects on one ormore interfaces associated with the online social network (e.g., therelated entities' profile pages). The social-networking system 160 mayaccess the content objects authored by the related entities to identifythose matching the text query and score the identified content objectsbased at least in part on social signals (e.g., likes, shares, comments)associated with each content object and its author.

The social-networking system 160 may provide the querying user withsearch results corresponding to content objects associated with theonline social network that match the inputted text query. In presentingthe search results, the social-networking system 160 may preferentiallypresent search results that correspond to content objects created by therelated entities to the primary entity Oculus. Within each of the searchresults corresponding to content objects authored by the relatedentities, the social-networking system 160 may further present a snippetdescribing a relationship of the author of the corresponding contentobject and the primary entity Oculus. For example, on a search-resultsinterface, the social-networking system 160 may display a post authoredby Brendan Iribe (CEO and co-founder of Oculus) about Oculus Rift (avirtual reality headset made by Oculus), along with one or more searchresults corresponding to posts authored by other entities (e.g., newsmedia, users) that may be unrelated to the primary entity Oculus. It maydisplay the post by Brendan Iribe along with a vital-author snippet“Brendan Iribe is co-founder and CEO of Oculus.” Although thisdisclosure describes presenting snippets describing relationshipsbetween a primary entity referenced by a search query and relatedentities authoring content objects matching the search query in aparticular manner, this disclosure contemplates presenting snippetsdescribing relationships between a primary entity referenced by a searchquery and related entities authoring content objects matching the searchquery in any suitable manner.

In particular embodiments, the social-networking system 160 may receivea text query from a client system 130 of a user of the online socialnetwork. The text query may comprise one or more n-grams inputted by theuser. The text query may be an unstructured text query. The text querymay be entered, for example, into a query field. The query field may bepresented to the user via a webpage displayed by a web browser 132 onthe user's client system 130 or via an application associated with theonline social network installed on the user's client system 130. Inparticular embodiments, the text query may comprise one or more n-gramsassociated with a trending topic included in a list of trending topics.The social-networking system 160 may continuously log and analyze theactivities of a plurality of users of the online social network such as,for example, searching, posting, commenting, sharing, another suitableactivity, or any combination thereof. The logging may be subject toprivacy settings of each of the users involved. By doing so, thesocial-networking system 160 may identify a plurality of topics that aretrending on the online social network at the moment. The topics maycomprise people, places, events, other topics, or any combinationthereof, which may be provided by a topic tagger service associated withthe online social network. The trending nature of a topic may beindicated by an uptick in social-networking activities related to thetopic. The identified topics may be compiled into a list of trendingtopics. More information on trending topics may be found in U.S. patentapplication Ser. No. 14/858,366, filed 18 Sep. 2015, which isincorporated by reference. The social-networking system 160 mayautomatically generate a text query in response to the user clicking ona trending topic provided for display to the user in a user interfaceassociated with the online social network. The text query comprisingn-grams inputted by the user may be transmitted from the user's clientsystem 130 to the social-networking system 160 via the network 110. Asan example and not by way of limitation, the user may input a text query“the avengers” into a query field associated with the social-networkingsystem 160 that is displayed on the client system 130 of the user. Thetext query may comprise at least the n-grams “the,” “avengers,” and “theavengers.” As another example and not by way of limitation, the user mayinput a text query “paris attack eagles of death metal” in the queryfield. This text query may comprise at least the n-grams “paris,” “parisattack,” “eagles of death metal,” and several other n-grams. Althoughthis disclosure describes receiving particular text queries in aparticular manner, this disclosure contemplates receiving any suitabletext queries in any suitable manner.

In particular embodiments, the social-networking system 160 may parsethe text query to identify a primary entity referenced in the textquery, wherein the primary entity matches one or more of the n-grams ofthe text query. The primary entities may comprise one or more of aperson, an organization, a place, a topic, or another suitable entity.The social-networking system 160 may identify a primary entity bycomparing the names of one or more entities with one or more n-grams ofthe received text query. The social-networking system 160 mayalternatively or additionally identify a primary entity by identifying auser node 202, a concept node 204, or an edge 206 on the social graph200 that matches one or more n-grams of the text query and identifyingan entity associated with the identified element of the social graph200. More information on identifying entities associated with textqueries based on social graph information may be found in U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, and U.S. patentapplication Ser. No. 15/192,780, filed 24 Jun. 2016, which areincorporated by reference. The social-networking system 160 mayalternatively or additionally identify a primary entity by identifying anode that matches one or more n-grams of the text query and inferring atopic based on the identified node. More information on identifyingtopics from text may be found in U.S. patent application Ser. No.13/167,701, filed 24 Jun. 2011, and U.S. patent application Ser. No.14/556,854, filed 1 Dec. 2014, which are incorporated by reference. Asan example and not by way of limitation, the social-networking system160 may parse a text query “the avengers” to identify a primary entityThe Avengers, which is an organization whose name matches the textquery. As another example and not by way of limitation, thesocial-networking system 160 may parse a text query “paris attack eaglesof death metal” to identify primary entities Paris (a place), ParisAttack (a topic referring to a series of terrorist attacks that occurredin Paris on 13 Nov. 2015), and Eagles of Death Metal (a music band whoseconcert was a scene of terrorist attack on 13 Nov. 2015). Although thisdisclosure describes parsing particular text queries to identifyparticular primary entities in a particular manner, this disclosurecontemplates parsing any suitable text queries to identify any suitableprimary entities in any suitable manner.

In particular embodiments, the social-networking system 160 may identifyone or more related entities for the primary entity based on one or morerelated-entity indexes associated with the primary entity. Eachrelated-entity index for the primary entity may comprise identificationinformation of one or more related entities to the primary entity. Eachrelated-entity index may further comprise a snippet comprisinginformation describing a relationship between the primary entity andeach related entity in the related-entity index. The social-networkingsystem 160 may pre-generate one or more related-entity indexes for eachof a plurality of entities associated with the online social network andstore such related-entity indexes in one or more data stores 164 of thesocial-networking system 160. The stored related-entity indexes may beupdated periodically or dynamically. Alternatively, thesocial-networking system 160 may generate one or more related-entityindexes in real time upon receiving a text query from the querying user.A related-entity index may comprise a plurality of entries, each mappingfrom identification information of the primary entity corresponding tothe index to identification information of one or more other entitiesthat are related to the primary entity or considered to be authoritativeor reliable information sources about the primary entity. Theidentification information may comprise a name, an identificationnumber, a hash key, other suitable identification information, or anycombination thereof. The related-entity indexes may be searchable by thesocial-networking system 160 to identify one or more related entitiesfor a particular primary entity. As an example and not by way oflimitation, the social-networking system 160 may store a related-entityindex associated with the entity Eagles of Death Metal, a music band.The related-entity index may comprise a plurality of entries. Each entrymay comprise identification information of Eagles of Death Metal andidentification information of an entity related to the band. The relatedentities whose identification information is included in therelated-entity index may comprise, for example, members of the band,famous songs or albums of the band, music commentators who havepublished on the band or the band's genre, other suitable entities, orany combination thereof.

In particular embodiments, each related-entity index may furthercomprise a snippet comprising information describing a relationshipbetween the primary entity and each related entity in the related-entityindex. Such a snippet may be referred to as a vital-author snippetherein. The snippet may be in the form of a text string comprising oneor more n-grams that are made comprehensible to the user. In particularembodiments, the snippet associated with each of the identified relatedentities may be subject to a length limit and comprise a number ofcharacters less than or equal to a specified character limit. The lengthlimit may be determined and enforced by the social-networking system 160based at least in part on a maximal number of characters displayable ina snippet field of a search-result interface associated with the onlinesocial network. As an example and not by way of limitation, an entry ofa particular related-entity index may map from identificationinformation of a primary entity Oculus to identification information ofa related entity Brendan Iribe. This entry may further include a snippet“Brendan Iribe is co-founder and CEO of Oculus,” which describes arelationship between the primary entity Oculus and the related entityBrendan Iribe. This example is further illustrated by FIG. 4, which isdescribed below. The related-entity indexes may be generated and storedusing one or more of a plurality of data structures. Such datastructures may comprise, for example, an array, a set, a tree, anothersuitable data structure, or any combination thereof. Although thisdisclosure describes using related-entity indexes to store informationabout primary entities, related entities, and vital-author snippets,this disclosure contemplates storing such information in any suitableformat.

FIG. 4 illustrates an example related-entity index. In particularembodiments, the social-networking system 160 may generate one or morerelated entity indexes for each of a plurality of entities associatedwith the online social network and store such related-entity indexes inone or more data stores 164 of the social-networking system 160.Specifically, the social-networking system 160 may generate arelated-entity index 400 for the entity Oculus. The entity maycorrespond to a particular concept node 204 on the social graph 200associated with the online social network. In the example illustrated inFIG. 4, the related-entity index 400 comprises identificationinformation for the entity Oculus 410. It further comprises a pluralityof entries each mapping from the identification information of Oculus410 to identification information 420 of entities determined to berelated to Oculus. In particular embodiments, the related entitiesincluded in the related-entity index 400 may be identified based on oneor more online encyclopedic index, social-graph information, one or morecontent objects associated with the online social network, anothersuitable source, or any combination thereof. As an example and not byway of limitation, referencing FIG. 4, the related-entity index 400 forthe entity Oculus 410 comprises identification information for BrendanIribe 420 a, Palmer Luckey 420 b, Facebook 420 c, Mark Zuckerberg 420 d,Surreal Vision 420 e, and identification information 420 for one or moreother entities determined to be related to Oculus. The related-entityindex 400 may further comprise a plurality of snippets 430 eachdescribing a relationship between the primary entity and one of theidentified related entities. The snippets 430 may be generated based ondata extracted from content objects that are available online. As anexample and not by way of limitation, the related-entity index 400 forthe entity Oculus 410 comprises a snippet “Brendan Iribe is co-founderand CEO of Oculus” 430 a, which describes a relationship between theprimary entity Oculus and the related entity Brendan Iribe. Therelated-entity index 400 for the entity Oculus 410 further comprisessnippets such as “Palmer Luckey is co-founder of Oculus” 430 b,“Facebook is the parent company of Oculus” 430 c, “Mark Zuckerberg isCEO of Oculus's parent” 430 d, “Surreal Vision is a subsidiary ofOculus” 430 e, and snippets 430 for one or more other related entities.Each snippet 430 may be stored, in the related-entity index 400, inassociation with identification information 420 of its correspondingrelated entity. Although this disclosure describes and FIG. 4illustrates a particular related-entity index, this disclosurecontemplates any suitable related-entity indexes.

In particular embodiments, the social-networking system 160 may generateone or more of the related-entity indexes based at least in part on oneor more online encyclopedic indexes (e.g., freebase.com, wikipedia.org).As an example and not by way of limitation, the social-networking system160 may access a Wikipedia page about the entity Eagles of Death Metaland search through the text and links included in the page to identifyinformation about other entities associated with the online socialnetwork. For example, the social-networking system 160 may determinethat Josh Homme is related to Eagles of Death Metal based on the factthat the accessed Wikipedia page contains a link to another Wikipediapage that corresponds to Josh Homme. The social-networking system 160may also determine that Jesse Hughes is related to Eagles of Death Metalbased on the fact that the former's name appears multiple times on theWikipedia page associated with the later. The social-networking system160 may further infer that the relationship between Jesse Hughes andEagles of Death Metal is that the former is a member of the latter basedon the fact that the former's name appears in a “Members” field on theWikipedia page. Based on the Wikipedia page, the social-networkingsystem 160 may determine that the entities Josh Homme and Jesse Hughesare likely to be authoritative or reliable sources of information withrespect to Eagles of Death Metal and thereby include theiridentification information in a related-entity index associated withEagles of Death Metal.

In particular embodiments, the social-networking system 160 mayadditionally generate one or more of the related-entity indexes based atleast in part on social-graph information associated with the primaryentity. For a particular primary entity, the social-networking system160 may identify one or more related entities that are connected to theprimary entity on the social graph 200 or have an affinity coefficientwith the primary entity within a particular range. As an example and notby way of limitation, the social-networking system 160 may access thesocial graph 200 and identify one or more nodes connected to the noderepresenting Oculus by one or more edges 206. The social-networkingsystem 160 may then access or calculate an affinity coefficient betweenOculus and each of the entities corresponding to the identified nodes.It may identify those entities whose affinity coefficients with Oculusare above a threshold value to be related entities to Oculus. Forexample, the social-networking system 160 may determine that a noderepresenting Brendan Iribe, co-founder of Oculus, is connected to thenode representing Oculus on the social graph 200 and that their affinitycoefficient is greater than a threshold. The social-networking system160 may accordingly include Brendan Iribe in a related-entity indexassociated with Oculus.

In particular embodiments, the social-networking system 160 may generateone or more of the related-entity indexes based at least in part on aco-occurrence of references to the primary entity with references to oneor more of the related entities within content associated with theonline social network. For a particular primary entity, thesocial-networking system 160 may extract names of one or more relatedentities from one or more content objects associated with the primaryentity on the online social network. The content objects may compriseprofile interfaces, posts, news stories, headlines, instant messages,chat room conversations, emails, advertisements, pictures, videos, audiofiles (e.g., music), other suitable objects, or any combination thereof.The content objects may comprise a reference (e.g., name, hash tag) tothe primary entity, be authored by the primary entity, or be included inan interface associated with the primary entity (e.g., the primaryentity's profile interface). As an example and not by way of limitation,the social-networking system 160 may access a plurality of contentobjects comprising references to the entity France on the online socialnetwork. It may determine that the name François Hollande, a referenceto the president of France, appears frequently in the accessed contentobjects. It may then extract this name and include it in arelated-entity index associated with the entity France. For a particularentity, the social-networking system 160 may generate different andseparate related-entity indexes, each associated with the entity, withdifferent ones of the aforementioned methods. Alternatively, thesocial-networking system 160 may compile identification information ofrelated entities identified based on various methods or sources into onerelated-entity index associated with the entity. More information onidentifying authors or related entities for topics may be found in U.S.patent application Ser. No. 14/554,190, filed 26 Nov. 2014, and U.S.patent application Ser. No. 15/365,113, filed 30 Nov. 2016, which areincorporated by reference.

In particular embodiments, the social-networking system 160 may generateone of the related-entity indexes for the primary entity based on one ormore entities determined to be similar to the primary entity.Specifically, the social-networking system 160 may identify one or moresimilar entities to the primary entity and populate the related-entityindex for the primary entity with one or more related entities extractedfrom one or more related-entity indexes associated with the identifiedsimilar entities. The similar entities to the primary entity may beidentified based on one or more categories, characteristics,affiliations, or social graph information associated with the similarentities. They may comprise, for a primary entity representing anindividual, one or more individuals sharing demographic attributes withthe primary entity, one or more individuals affiliated with a sameorganization as the primary entity, one or more individuals sharing asubstantial number of social connections with the primary entity, othersuitable entities, or any combination thereof. The similar entities maycomprise, for a primary entity representing an organization, one or moreorganizations of a same type (e.g., non-profit organizations) as theprimary entity, one or more organizations engaging in a same industry asthe primary entity, one or more organizations located at a same city asthe primary entity, other suitable entities, or any combination thereof.The similar entities may comprise, for a primary entity corresponding toa topic, one or more topics of a same type, related to a same event,covering a same individual or organization, or having another area ofsimilarity with the primary entity. It may be the case that an entityknowledgeable about a particular topic is likely to be knowledgeableabout a similar topic. This may motivate “borrowing” related entitiesfrom a similar entity's related-entity indexes. As an example and not byway of limitation, for the primary entity Iron Man (a superhero figurefrom a fictional story The Avengers in comic books and films), thesocial-networking system 160 may identify one or more similar entitiesincluding, for example, Captain America. The social-networking system160 may determine that the two entities are similar based on the factthat both are superhero figures in a same story. The social-networkingsystem 160 may thereby extract one or more related entities from one ormore related-entity indexes associated with Captain America and includethem in a related-entity index associated with Iron Man. For example,the extracted related entities may comprise Joss Whedon, a director ofThe Avengers films, which include both Iron Man and Captain America ascharacters.

In particular embodiments, the social-networking system 160 maygenerate, for each related entity in one of the related-entity indexesassociated with the primary entity, a snippet based on data extractedfrom a content object associated with the primary entity and the relatedentity. The content object may be associated with the online socialnetwork. It may comprise, for example, a post, a news story, a profileinterface, an instant message, a group/chat room conversation, an email,an advertisement, or another suitable object. The content object may beassociated with the primary and related entities because it is authoredby one or more of the entities, is posted on one or more interfacesassociated with one or more of the entities on the online socialnetwork, or comprises one or more references to one or more of theentities. Alternatively or additionally, the content object may beassociated with an online encyclopedic index. As an example and not byway of limitation, the content object may be a Wikipedia pagecorresponding to the primary entity and mentioning the related entity.The social-networking system 160 may use a natural-language processingalgorithm to analyze the extracted data in generating the snippet. Thenatural-language processing algorithm may be capable of performing taskssuch as, for example, automatic summarization, discourse analysis, namedentity recognition, national language understanding and generation,parsing, relationship extraction, another suitable natural-languageprocessing task, or any combination thereof. The social-networkingsystem 160 may then store the snippet in association with identificationinformation of the related entity in the related entity index. Thestored snippet may be updated periodically or dynamically.

In particular embodiments, the data extracted by the social-networkingsystem 160 may comprise one or more text strings. The social-networkingsystem 160, in generating a snippet based on the extracted data, mayremove one or more phrases from one or more of the text stings, add oneor more phrases to one or more of the text strings, change an order ofone or more phrases within one or more of the text strings, or combineone or more of the text strings. The social-networking system 160 maydetermine that one or more text strings or sentences within a contentobject are related to the primary entity and/or the related entity. Thetext strings or sentences may be determined to be related to an entitybased on that they mention the entity or that they are in the context ofa sentence mentioning the entity. It may extract such text strings fromthe content object and analyze the text strings to generate a snippetdescribing the relationship between the primary entity and the relatedentity. The social-networking system 160 may directly use one or more ofthe extracted sentences as such a snippet. It may alternatively modifythe extracted sentences to create a vital-author snippet. In modifyingthe extracted sentences, the social-networking system 160 may remove oneor more phrases from an extracted sentence, add one or more phrases toan extracted sentence, change an order of one or more phrases in anextracted sentence, combine one or more different sentences, or performanother suitable modification. The modifications may be based on ananalysis of the structure or meaning of one or more of the sentencesusing a natural-language processing algorithm. As an example and not byway of limitation, for a primary entity Oculus and a related entityBrendan Iribe, the social-networking system 160 may access a Wikipediapage corresponding to Oculus that mentions Brendan Iribe. It mayidentify from the Wikipedia page text strings “Brendan Iribe is the CEOof Oculus” and “Oculus was founded by Palmer Luckey and Brendan Iribe.”The social-networking system 160 may directly use the former sentence“Brendan Iribe is the CEO of Oculus” as a snippet and store the snippetin association with identification information of Brendan Iribe in arelated-entity index associated with Oculus. Alternatively, thesocial-networking system 160 may analyze the meaning of the lattersentence to infer that Brendan Iribe is a co-founder of Oculus andmodify the sentence to be “Palmer Luckey and Brendan Iribe co-foundedOculus” by adding, removing, and changing the order of one or morephrases. It may further combine the two sentences to generate thesnippet “Brendan Iribe is co-founder and CEO of Oculus.” As anotherexample and not by way of limitation, for a primary entity Facebook anda related entity Mark Zuckerberg, the social-networking system 160 mayaccess a content object associated with Mark Zuckerberg on the onlinesocial network to identify a text string “He is the chairman, chiefexecutive officer, and co-founder of social networking websiteFacebook.” The social-networking system may add a phrase “MarkZuckerberg” to the extracted text string and remove the phrases “He,”“chief executive officer,” “co-founder,” and “social networking website”from the text string to generate the snippet “Mark Zuckerberg is thechairman of Facebook.” In particular embodiments, a grammar model may beused to generate natural-language strings for the vital-author snippets.More information on grammar models may be found in U.S. patentapplication Ser. No. 13/674,695, filed 12 Nov. 2012, and U.S. patentapplication Ser. No. 13/731,866, filed 31 Dec. 2012, which areincorporated by reference.

In particular embodiments, the data extracted by the social-networkingsystem 160 may comprise structured data indicating a relationshipbetween the primary entity and a related entity. The social-networkingsystem 160, in generating a snippet based on the extracted data, maycomplete a pre-generated snippet template using the structured data. Thesocial-networking system 160 may store one or more pre-generatedtemplates for generating snippets to be included in related-entityindexes. Such a template may comprise one or more text strings to beshared by multiple snippets. It may further comprise one or moreplaceholders, each placeholder representing a blank component of thetemplate that may be replaced or filled in with a reference to anentity. Each placeholder may specify one or more criteria that an entitymust satisfy in order to be referenced in a snippet generated based onthe template. The social-networking system 160 may extract structureddata indicating a relationship between the primary entity and a relatedentity from a content object. It may select or apply one or more snippettemplates based on the structured data. This functionality may beparticularly useful for generating snippets for certain types ofentities (e.g., locations, countries). As an example and not by way oflimitation, for a primary entity Mark Zuckerberg and a related entityFacebook, the social-networking system 160 may access a profileinterface (e.g., a background or “about” page) of Mark Zuckerberg anddetermine that the term “Facebook” appears in the “work” field, which isa field displaying a person's work information. Alternatively, thesocial-networking system 160 may access the social graph 200 todetermine that a user node 202 associated with Mark Zuckerberg and aconcept node 204 associated with Facebook are connected by aworks-at-type edge 206. Based on such determination, thesocial-networking system 160 may select a template “[ ] works at [ ],”which comprises a text string “works at” and two placeholders. Based onthe structured data, the social-networking system 160 may fill “MarkZuckerberg” in the first blank and “Facebook” in the second blank tocomplete this template and generate the snippet “Mark Zuckerberg worksat Facebook.” As another example and not by way of limitation, for aprimary entity United States and a related entity Barack Obama, thesocial-networking system 160 may access a Wikipedia page correspondingto the United States as a country and determine that “Barack Obama”appears in the “President” field. Based on such structured data, thesocial-networking system 160 may complete a template “[ ] is thePresident of H” and generate the snippet “Barack Obama is the Presidentof the United States.”

In particular embodiments, for each related entity in one of therelated-entity indexes associated with the primary entity, thesocial-networking system 160 may generate a plurality of snippets basedon data extracted from one or more content objects associated with theprimary entity and the related entity. The social-networking system 160may then calculate, using a machine-learning model, a confidence scorefor each of the generated snippets based at least in part on one or morefactors. The factors may comprise, for example, a measure of reliabilityof the content object associated with the snippet, a measure of strengthof a relationship between the primary entity and the related entitydescribed by the snippet. The social-networking system 160 may thenstore one of the generated snippets having the greatest confidence scorein association with identification information of the related entity inthe related-entity index. In particular embodiments, thesocial-networking system 160 may access multiple content objects toextract data for generating a snippet describing a relationship betweena primary entity and a related entity. Because the content objects andthe data extracted from the content objects may be different, thesocial-networking system 160 may generate a plurality of differentsnippets all describing relationships between the two entities. It maybe desirable to quantitatively compare the different snippets and selectto store only the most favored snippet in a related-entity indexassociated with the primary entity and comprising identificationinformation of the related entity. As an example and not by way oflimitation, for a primary entity Mark Zuckerberg and a related entityFacebook, the social-networking system 160 may have generated twodifferent snippets. It may have generated a first snippet “MarkZuckerberg is CEO of Facebook” based on a Wikipedia page correspondingto Facebook. It may have also generated a second snippet “MarkZuckerberg works at Facebook” based on a user's post on the onlinesocial network. The social-networking system 160 may calculate aconfidence score for each of the two snippets. The confidence scores maybe based on a measure of reliability of the content objects associatedwith the snippets. For example, the social-networking system 160 maytreat Wikipedia as a more reliable source than an ordinary post on theonline social network and thus calculate a greater confidence score forthe first snippet than the second snippet. The confidence scores mayalso be based on a measure of strength of a relationship between theprimary entity and the related entity described by each of the snippets.For example, the social-networking system 160 may treat the firstsnippet, which describes a specific position of an individual in anorganization, as describing a stronger relationship than the secondsnippet, which generally describes where the individual works. Thesocial-networking system 160 may therefore calculate a greaterconfidence score for the first snippet than the second snippet. Thesocial-networking system 160 may choose to store the first snippet in arelated-entity index because it has a higher confidence score than thesecond snippet. Although this disclosure describes identifyingparticular related entities for particular primary entities in aparticular manner, this disclosure contemplates identifying any suitablerelated entities for any suitable primary entities in any suitablemanner.

In particular embodiments, the social-networking system 160 may identifyone or more content objects matching the text query. Each identifiedcontent object may be associated with one or more of the relatedentities identified in the related-entity indexes of the primary entity.The social-networking system 160 may search one or more data stores 164storing content objects authored by the identified related entities forthose that match all of the n-grams of the received text query.Alternatively or additionally, the social-networking system 160 maysearch the data stores 164 for content objects authored by theidentified related entities that match only one or more of the n-gramsof the received text query (i.e., partial matches). One or more of theidentified content objects may be published on one or more profileinterfaces corresponding to one or more of the identified relatedentities, respectively. One or more of the identified content objectsmay alternatively be stored or published on one or more third-partysystems 170. Such content objects may be referenced on the online socialnetwork through one or more URL links. Although this disclosuredescribes identifying particular content objects in a particular manner,this disclosure contemplates identifying any suitable content objects inany suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a score for each of the identified content objects based atleast in part on a number of social signals associated with the contentobject and one or more related entities associated with the contentobject. The social-networking system 160 may rank the identified contentobjects based on their corresponding scores. The social-networkingsystem 160 may then generate one or more search results comprisingreferences to one or more of the identified content objects,respectively. Each content object associated with a search results mayhave a calculated score greater than a threshold score. The socialsignals associated with a content object may comprise one or more of anumber of likes associated with the content object, a number of commentson the content object, a number of shares of the content object, achange in a growth rate of an amount of social activities (e.g., likes,comments, shares, views) associated with the content object, or anothersuitable social signal. The social-networking system 160 maypreferentially score a content object that has received extensiveattentions or interactions from users of the online social network, assuch a content object may likely be of interest to the querying user. Asan example and not by way of limitation, the social-networking system160 may identify a first post that has been liked 1000 times, commentedon 500 times, and shared 300 times and a second post that has been liked500 times, commented on 300 times, and shared 200 time. Both posts maymatch all n-grams of the received text query and be authored by a samerelated entity. The social-networking system 160 may calculate a greaterscore for the first post than for the second post based on the socialsignals associated with the posts. The social-networking system 160 mayalso preferentially score a content object the amount of socialactivities associated with which has seen an uptick or a spike.Continuing the preceding example, the first post may have been publishedon the online social network for twenty days and the likes, comments,and shares for this first post may have been increasing at a roughlysteady rate. The amount of social interactions for the second post,however, may have recently gone through a sudden increase due to a pieceof breaking news related to the second post. The number of likes for thesecond post, for example, may have increased from 10 to 500 within thepast day. Based on this significant change in the growth rate of theamount of social activities associated with the second post, thesocial-networking system 160 may calculate a higher score for the secondpost than the first post although the latter has a greater amount ofsocial activities.

The social signals associated with a related entity may comprise one ormore of a number of followers for the related entity, a number of visitsto a profile interface corresponding to the related entity, a number oflikes associated with the related entity, or another suitable socialsignal. It may be the case that the querying user is likely to beinterested in a content object authored by an entity that is popular orfamous on the online social network. As an example and not by way oflimitation, the social-networking system 160 may identify a contentobject that is authored by a first related entity and a content objectthat is authored by a second related entity. The first related entitymay be a celebrity who has a large number of followers on the onlinesocial network. Many users visit a profile interface associated with thefirst related entity and interact with the content objects postedtherein every day. The second related entity, on the other hand, may bean ordinary user of the online social network. The social-networkingsystem 160 may calculate a greater score for the first content objectthan for the second content object based at least in part on the socialsignals associated with their respective authors.

In particular embodiments, the social-networking system 160 maycalculate the score for an identified content object further based onone or more other factors. These other factors, in particular, maycomprise a level of matching between the content object and the receivedtext query, a level of authoritativeness of the related entity authoringthe content object with respect to the primary entity, another suitablefactor, or any combination thereof. For a particular content object, thesocial-networking system 160 may determine the level of matching betweenthe content object and the received text query by searching one or moren-grams of the text query against the content. It may determine a numberof times that each n-gram of the received text query appears in thecontent object. A higher number of occurrences of one or more componentn-grams of the received text query in the identified content object maycorrespond to a higher level of matching of the content object with thequery. The social-networking system 160 may calculate a higher score fora content object that matches the received text query closely.

In particular embodiments, the social-networking system 160 maycalculate the score for an identified content object further based on anintent-matching process. Specifically, the social-networking system 160may determine a first meaning of one of the n-grams of the text query,determine a second meaning of an n-gram within the context of thecontent object (e.g., content of the content object, tags placed on thecontent object), and calculate the score based at least in part on alevel of matching between the first meaning and the second meaning. Thisfunctionality may help ensure that content objects retrieved based onthe user's text query contain information that the user intends toobtain. As an example and not by way of limitation, the user may input atext query “apple new products” to search against the online socialnetwork. In response to this query, the social-networking system 160 mayidentify at least a first content object about a new Apple laptop modeland a second content object about a new apple juice product. Thesocial-networking system 160 may use the intent-matching process todetermine the meaning of the term “apple” as used by the user. Based atleast in part on a search history associated with the user and analysisof the structure of the text query, the social-networking system 160 maydetermine that “apple” as used by the user is likely to refer to anAmerican technology company (Apple Inc.) rather a fruit. Thesocial-networking system 160 may determine that “apple,” as used in thefirst content object, refers to the technology company. “Apple,” as usedin the second content object, however, refers to a type of fruit. Themeaning of “apple” in the first content object has a higher level ofmatching with its likely meaning in the text query than the meaning of“apple” in the second content object. The social-networking system 160may calculate a greater score for the first content object than thesecond content object.

The calculated score may be a function of any combination of the factorsdescribed above. As an example and not by way of limitation, thefunction for calculating a confidence score c may be represented by thefollowing expression: c=f(m₁, m₂, m₃), where m₁, m₂, and m₃ are threedifferent factors. The calculated confidence score c may alternativelybe a sum of different functions that may be weighted in a suitablemanner (e.g., the weights being pre-determined by the social-networkingsystem 160). As an example and not by way of limitation, the functionfor calculating a confidence score c may be represented by the followingexpression: c=A f₁(m₁, m₂)+B f₂(m₃), where m₁, m₂, and m₃ are threedifferent factors, and where A and B are two different weights. Althoughthis disclosure describes calculating particular scores for particularcontent objects in a particular manner, this disclosure contemplatescalculating any suitable scores for any suitable content objects in anysuitable manner.

In particular embodiments, the social-networking system 160 may send, tothe client system 130 responsive to receiving the text query,instructions for presenting one or more search results corresponding toone or more of the identified content objects, respectively. Each searchresult may comprise a reference to the associated related entity and asnippet for the related entity describing the relationship between theprimary entity and the related entity. The social-networking system 160may select one or more of the identified content objects for display tothe user based at least in part on the scores calculated for the contentobjects. The social-networking system 160 may store a pre-determinedthreshold score. It may alternatively generate the threshold score inreal time based on the scores calculated for the identified contentobjects. The threshold score may have an absolute value or a dynamicvalue. The dynamic value may be defined such that search resultscorresponding to a certain number of top-ranked content objects (e.g.,top 7) are sent to the client system 130. The social-networking system160 may compare the score calculated for each of the identified contentobjects with the threshold score and identify those content objects thathave scores greater than the threshold score.

The social-networking system 160 may then generate a search result foreach content object having an above-threshold score. The search resultmay comprise, for example, a title of the content object, an excerptfrom the content of the content object, an image associated with thecontent object, information about an author of the content object, oneor more interactive elements allowing a user of the online socialnetwork to interact with the content object (e.g., like, comment,share), another suitable component, or any combination thereof. Theinformation about the author of the content object may comprise, forexample, a reference to a related entity authoring the content object ora snippet for the related entity describing the relationship between theprimary entity and the related entity. The snippet may have beenobtained from a related-entity index associated with the primary entityand comprising information about the related entity authoring thecontent object. The instructions sent by the social-networking system160 may specify that the snippet is to be displayed as part of asearch-result module associated with the search result or in proximityto the search-result module within a search-results interface. Each ofone or more of the search results generated by the social-networkingsystem 160 may further comprise one or more snippets formulated in otherformats or comprising other information related to the content objectassociated with the search result, primary entities associated with thetext query, or the related entity authoring the content object. Moreinformation on snippets may be found in U.S. patent application Ser. No.13/827,214, filed 14 Mar. 2013, U.S. patent application Ser. No.14/797,819, filed 13 Jul. 2015, U.S. patent application Ser. No.14/938,685, filed 11 Nov. 2015, U.S. patent application Ser. No.14/996,937, filed 15 Jan. 2016, and U.S. patent application Ser. No.15/365,113, filed 30 Nov. 2016, which are incorporated by reference.

The search results may be displayed on a webpage associated with theonline social network accessed by a web browser 132 on the client system130 of the querying user. The search results may alternatively bedisplayed in a user interface associated with an applicationcorresponding to the social-networking system 160 that is installed onthe client system 130 of the querying user. The client system 130 maydisplay to the querying user a search-results interface that onlycomprise one or more search results corresponding to one or more contentobjects authored by one or more of the identified related entities. Thesearch results may be presented in a ranked order based at least in parton the respective scores of the content objects referenced by the searchresults. Specifically, a search result corresponding to a content objecthaving a higher score may be displayed at a more noticeable positionwithin the interface displaying the search results (e.g., at the top ofthe interface). Alternatively, the search results corresponding tocontent objects authored by the identified related entities may beaggregated with one or more other search results, which may correspondto other content objects associated with the online social network. Suchother content objects may be authored by other entities on the onlinesocial network (e.g., news media, users). Search results correspondingto content objects authored by related entities may constitute aparticular percentage of all search results provided for display to theuser. This percentage may be specified and enforced by thesocial-networking system 160. The aggregated search results may becollectively displayed to the querying user in a search-resultsinterface on the client system 130. In this case, the search resultscorresponding to content objects authored by identified related entitiesmay be given priority as against the other search results when displayedto the querying user. Although this disclosure describes sendinginstructions for presenting particular search results in a particularmanner, this disclosure contemplates sending instructions for presentingany suitable search results in any suitable manner.

FIG. 5 illustrates an example user interface displaying example searchresults. In particular embodiments, responsive to a search attempt by aquerying user, the client system 130 of the user may display asearch-results interface 500 comprising one or more search results 530.The information displayed may have been received from thesocial-networking system 160. The user interface may comprise a queryfield 510. In this example, the querying user may have inputted a textquery “oculus new headset” in the query field 510 to search against theonline social network. The social-networking system 160 may parse thereceived text query to identify a primary entity referenced in the textquery that matches one or more of the n-grams of the text query. Theprimary entity identified in this example may be Oculus. Thesocial-networking system 160 may identify one or more related entitiesfor the primary entity and identify content objects associated with oneor more of the related entities that match the text query. As an exampleand not by way of limitation, Brendan Iribe and Mark Zuckerberg may beidentified as such related entities. The social-networking system 160may send one or more search results corresponding to content objectsauthored by the related entities to the client system 130 of thequerying user for display. The search result 530 a may correspond to apost by Brendan Iribe, who may be one of the identified relatedentities. The search result 530 a may comprise identificationinformation for the associated related entity as well as a snippet 430 adescribing a relationship between the related entity Brendan Iribe andthe primary entity Oculus. The snippet 430 a may have been obtained fromthe related-entity index 400. The search result 530 b may correspond toa post by Mark Zuckerberg, who may also be one of the identified relatedentities. The search result 530 b may comprise identificationinformation for the associated related entity as well as a snippet 430 ddescribing a relationship between the related entity Mark Zuckerberg andthe primary entity Oculus. The snippet 430 d may have been obtained fromthe related-entity index 400. The search results 530 may be organized inthis search-results interface 500 based at least in part on theircorresponding scores calculated by the social-networking system 160. Thesearch-results interface 500 may further comprise a filter bar 520allowing the user to filter the search results 530 based on, forexample, author, location, and date of content objects associated withthe search results 530. Although FIG. 5 illustrates displayingparticular search results in a particular user interface in a particularmanner, this disclosure contemplates displaying any suitable searchresults in any suitable user interface in any suitable manner.

FIG. 6 illustrates an example method 600 for providing search resultscorresponding to content objects and comprising snippets describingrelationships between authors of the content objects and entitiesreferenced by a text query. The method may begin at step 610, where thesocial-networking system 160 may receive a text query comprising one ormore n-grams inputted by a first user of an online social network. Atstep 620, the social-networking system 160 may parse the text query toidentify a primary entity referenced in the text query, wherein theprimary entity matches one or more of the n-grams of the text query. Atstep 630, the social-networking system 160 may identifying one or morerelated entities for the primary entity based on one or morerelated-entity indexes associated with the primary entity, wherein eachrelated-entity index for the primary entity comprises identificationinformation of one or more related entities to the primary entity and,for each related entity in the related-entity index, a snippetcomprising information describing a relationship between the primaryentity and the related entity. At step 640, the social-networking system160 may identify one or more content objects matching the text query,each identified content object being associated with one or more of therelated entities identified in the related-entity indexes of the primaryentity. At step 650, the social-networking system 160 may sendinstructions for presenting one or more search results corresponding toone or more of the identified content objects, respectively, each searchresult comprising a reference to the associated related entity and asnippet for the related entity describing the relationship between theprimary entity and the related entity. Particular embodiments may repeatone or more steps of the method of FIG. 6, where appropriate. Althoughthis disclosure describes and illustrates particular steps of the methodof FIG. 6 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 6 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for providing search results correspondingto content objects and comprising snippets describing relationshipsbetween authors of the content objects and entities referenced by a textquery including the particular steps of the method of FIG. 6, thisdisclosure contemplates any suitable method for providing search resultscorresponding to content objects and comprising snippets describingrelationships between authors of the content objects and entitiesreferenced by a text query including any suitable steps, which mayinclude all, some, or none of the steps of the method of FIG. 6, whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 6, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 6.

Social Graph Affinity and Coefficient

In particular embodiments, the social-networking system 160 maydetermine the social-graph affinity (which may be referred to herein as“affinity”) of various social-graph entities for each other. Affinitymay represent the strength of a relationship or level of interestbetween particular objects associated with the online social network,such as users, concepts, content, actions, advertisements, other objectsassociated with the online social network, or any suitable combinationthereof. Affinity may also be determined with respect to objectsassociated with third-party systems 170 or other suitable systems. Anoverall affinity for a social-graph entity for each user, subjectmatter, or type of content may be established. The overall affinity maychange based on continued monitoring of the actions or relationshipsassociated with the social-graph entity. Although this disclosuredescribes determining particular affinities in a particular manner, thisdisclosure contemplates determining any suitable affinities in anysuitable manner.

In particular embodiments, the social-networking system 160 may measureor quantify social-graph affinity using an affinity coefficient (whichmay be referred to herein as “coefficient”). The coefficient mayrepresent or quantify the strength of a relationship between particularobjects associated with the online social network. The coefficient mayalso represent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated at least in part on the history of the user's actions.Coefficients may be used to predict any number of actions, which may bewithin or outside of the online social network. As an example and not byway of limitation, these actions may include various types ofcommunications, such as sending messages, posting content, or commentingon content; various types of observation actions, such as accessing orviewing profile interfaces, media, or other suitable content; varioustypes of coincidence information about two or more social-graphentities, such as being in the same group, tagged in the samephotograph, checked-in at the same location, or attending the sameevent; or other suitable actions. Although this disclosure describesmeasuring affinity in a particular manner, this disclosure contemplatesmeasuring affinity in any suitable manner.

In particular embodiments, the social-networking system 160 may use avariety of factors to calculate a coefficient. These factors mayinclude, for example, user actions, types of relationships betweenobjects, location information, other suitable factors, or anycombination thereof. In particular embodiments, different factors may beweighted differently when calculating the coefficient. The weights foreach factor may be static or the weights may change according to, forexample, the user, the type of relationship, the type of action, theuser's location, and so forth. Ratings for the factors may be combinedaccording to their weights to determine an overall coefficient for theuser. As an example and not by way of limitation, particular useractions may be assigned both a rating and a weight while a relationshipassociated with the particular user action is assigned a rating and acorrelating weight (e.g., so the weights total 100%). To calculate thecoefficient of a user towards a particular object, the rating assignedto the user's actions may comprise, for example, 60% of the overallcoefficient, while the relationship between the user and the object maycomprise 40% of the overall coefficient. In particular embodiments, thesocial-networking system 160 may consider a variety of variables whendetermining weights for various factors used to calculate a coefficient,such as, for example, the time since information was accessed, decayfactors, frequency of access, relationship to information orrelationship to the object about which information was accessed,relationship to social-graph entities connected to the object, short- orlong-term averages of user actions, user feedback, other suitablevariables, or any combination thereof. As an example and not by way oflimitation, a coefficient may include a decay factor that causes thestrength of the signal provided by particular actions to decay withtime, such that more recent actions are more relevant when calculatingthe coefficient. The ratings and weights may be continuously updatedbased on continued tracking of the actions upon which the coefficient isbased. Any type of process or algorithm may be employed for assigning,combining, averaging, and so forth the ratings for each factor and theweights assigned to the factors. In particular embodiments, thesocial-networking system 160 may determine coefficients usingmachine-learning algorithms trained on historical actions and past userresponses, or data farmed from users by exposing them to various optionsand measuring responses. Although this disclosure describes calculatingcoefficients in a particular manner, this disclosure contemplatescalculating coefficients in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on a user's actions. The social-networkingsystem 160 may monitor such actions on the online social network, on athird-party system 170, on other suitable systems, or any combinationthereof. Any suitable type of user actions may be tracked or monitored.Typical user actions include viewing profile interfaces, creating orposting content, interacting with content, tagging or being tagged inimages, joining groups, listing and confirming attendance at events,checking-in at locations, liking particular interfaces, creatinginterfaces, and performing other tasks that facilitate social action. Inparticular embodiments, the social-networking system 160 may calculate acoefficient based on the user's actions with particular types ofcontent. The content may be associated with the online social network, athird-party system 170, or another suitable system. The content mayinclude users, profile interfaces, posts, news stories, headlines,instant messages, chat room conversations, emails, advertisements,pictures, video, music, other suitable objects, or any combinationthereof. The social-networking system 160 may analyze a user's actionsto determine whether one or more of the actions indicate an affinity forsubject matter, content, other users, and so forth. As an example andnot by way of limitation, if a user frequently posts content related to“coffee” or variants thereof, the social-networking system 160 maydetermine the user has a high coefficient with respect to the concept“coffee”. Particular actions or types of actions may be assigned ahigher weight and/or rating than other actions, which may affect theoverall calculated coefficient. As an example and not by way oflimitation, if a first user emails a second user, the weight or therating for the action may be higher than if the first user simply viewsthe user-profile interface for the second user.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on the type of relationship betweenparticular objects. Referencing the social graph 200, thesocial-networking system 160 may analyze the number and/or type of edges206 connecting particular user nodes 202 and concept nodes 204 whencalculating a coefficient. As an example and not by way of limitation,user nodes 202 that are connected by a spouse-type edge (representingthat the two users are married) may be assigned a higher coefficientthan a user nodes 202 that are connected by a friend-type edge. In otherwords, depending upon the weights assigned to the actions andrelationships for the particular user, the overall affinity may bedetermined to be higher for content about the user's spouse than forcontent about the user's friend. In particular embodiments, therelationships a user has with another object may affect the weightsand/or the ratings of the user's actions with respect to calculating thecoefficient for that object. As an example and not by way of limitation,if a user is tagged in a first photo, but merely likes a second photo,the social-networking system 160 may determine that the user has ahigher coefficient with respect to the first photo than the second photobecause having a tagged-in-type relationship with content may beassigned a higher weight and/or rating than having a like-typerelationship with content. In particular embodiments, thesocial-networking system 160 may calculate a coefficient for a firstuser based on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, the social-networking system 160may determine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph200. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 200 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 200.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on location information. Objects that aregeographically closer to each other may be considered to be more relatedor of more interest to each other than more distant objects. Inparticular embodiments, the coefficient of a user towards a particularobject may be based on the proximity of the object's location to acurrent location associated with the user (or the location of a clientsystem 130 of the user). A first user may be more interested in otherusers or concepts that are closer to the first user. As an example andnot by way of limitation, if a user is one mile from an airport and twomiles from a gas station, the social-networking system 160 may determinethat the user has a higher coefficient for the airport than the gasstation based on the proximity of the airport to the user.

In particular embodiments, the social-networking system 160 may performparticular actions with respect to a user based on coefficientinformation. Coefficients may be used to predict whether a user willperform a particular action based on the user's interest in the action.A coefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, the social-networking system 160 may provideinformation that is relevant to user's interests and currentcircumstances, increasing the likelihood that they will find suchinformation of interest. In particular embodiments, thesocial-networking system 160 may generate content based on coefficientinformation. Content objects may be provided or selected based oncoefficients specific to a user. As an example and not by way oflimitation, the coefficient may be used to generate media for the user,where the user may be presented with media for which the user has a highoverall coefficient with respect to the media object. As another exampleand not by way of limitation, the coefficient may be used to generateadvertisements for the user, where the user may be presented withadvertisements for which the user has a high overall coefficient withrespect to the advertised object. In particular embodiments, thesocial-networking system 160 may generate search results based oncoefficient information. Search results for a particular user may bescored or ranked based on the coefficient associated with the searchresults with respect to the querying user. As an example and not by wayof limitation, search results corresponding to objects with highercoefficients may be ranked higher on a search-results interface thanresults corresponding to objects having lower coefficients.

In particular embodiments, the social-networking system 160 maycalculate a coefficient in response to a request for a coefficient froma particular system or process. To predict the likely actions a user maytake (or may be the subject of) in a given situation, any process mayrequest a calculated coefficient for a user. The request may alsoinclude a set of weights to use for various factors used to calculatethe coefficient. This request may come from a process running on theonline social network, from a third-party system 170 (e.g., via an APIor other communication channel), or from another suitable system. Inresponse to the request, the social-networking system 160 may calculatethe coefficient (or access the coefficient information if it haspreviously been calculated and stored). In particular embodiments, thesocial-networking system 160 may measure an affinity with respect to aparticular process. Different processes (both internal and external tothe online social network) may request a coefficient for a particularobject or set of objects. The social-networking system 160 may provide ameasure of affinity that is relevant to the particular process thatrequested the measure of affinity. In this way, each process receives ameasure of affinity that is tailored for the different context in whichthe process will use the measure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632,869, filed 1 Oct. 2012, each of which isincorporated by reference.

Privacy

In particular embodiments, one or more of the content objects of theonline social network may be associated with a privacy setting. Theprivacy settings (or “access settings”) for an object may be stored inany suitable manner, such as, for example, in association with theobject, in an index on an authorization server, in another suitablemanner, or any combination thereof. A privacy setting of an object mayspecify how the object (or particular information associated with anobject) can be accessed (e.g., viewed or shared) using the online socialnetwork. Where the privacy settings for an object allow a particularuser to access that object, the object may be described as being“visible” with respect to that user. As an example and not by way oflimitation, a user of the online social network may specify privacysettings for a user-profile interface that identify a set of users thatmay access the work experience information on the user-profileinterface, thus excluding other users from accessing the information. Inparticular embodiments, the privacy settings may specify a “blockedlist” of users that should not be allowed to access certain informationassociated with the object. In other words, the blocked list may specifyone or more users or entities for which an object is not visible. As anexample and not by way of limitation, a user may specify a set of usersthat may not access photos albums associated with the user, thusexcluding those users from accessing the photo albums (while alsopossibly allowing certain users not within the set of users to accessthe photo albums). In particular embodiments, privacy settings may beassociated with particular social-graph elements. Privacy settings of asocial-graph element, such as a node or an edge, may specify how thesocial-graph element, information associated with the social-graphelement, or content objects associated with the social-graph element canbe accessed using the online social network. As an example and not byway of limitation, a particular concept node 204 corresponding to aparticular photo may have a privacy setting specifying that the photomay only be accessed by users tagged in the photo and their friends. Inparticular embodiments, privacy settings may allow users to opt in oropt out of having their actions logged by the social-networking system160 or shared with other systems (e.g., a third-party system 170). Inparticular embodiments, the privacy settings associated with an objectmay specify any suitable granularity of permitted access or denial ofaccess. As an example and not by way of limitation, access or denial ofaccess may be specified for particular users (e.g., only me, myroommates, and my boss), users within a particular degrees-of-separation(e.g., friends, or friends-of-friends), user groups (e.g., the gamingclub, my family), user networks (e.g., employees of particularemployers, students or alumni of particular university), all users(“public”), no users (“private”), users of third-party systems 170,particular applications (e.g., third-party applications, externalwebsites), other suitable users or entities, or any combination thereof.Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and may only be sent tothe user (or a client system 130 of the user) if the authorizationserver determines that the user is authorized to access the object basedon the privacy settings associated with the object. If the requestinguser is not authorized to access the object, the authorization servermay prevent the requested object from being retrieved from the datastore 164, or may prevent the requested object from being sent to theuser. In the search query context, an object may only be generated as asearch result if the querying user is authorized to access the object.In other words, the object must have a visibility that is visible to thequerying user. If the object has a visibility that is not visible to theuser, the object may be excluded from the search results. Although thisdisclosure describes enforcing privacy settings in a particular manner,this disclosure contemplates enforcing privacy settings in any suitablemanner.

Systems and Methods

FIG. 7 illustrates an example computer system 700. In particularembodiments, one or more computer systems 700 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 700 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 700 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 700.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems700. This disclosure contemplates computer system 700 taking anysuitable physical form. As example and not by way of limitation,computer system 700 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, or acombination of two or more of these. Where appropriate, computer system700 may include one or more computer systems 700; be unitary ordistributed; span multiple locations; span multiple machines; spanmultiple data centers; or reside in a cloud, which may include one ormore cloud components in one or more networks. Where appropriate, one ormore computer systems 700 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 700 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 700 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 700 includes a processor 702,memory 704, storage 706, an input/output (I/O) interface 708, acommunication interface 710, and a bus 712. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 702 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 702 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 704, or storage 706; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 704, or storage 706. In particular embodiments, processor702 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 702 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 702 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 704 or storage 706, andthe instruction caches may speed up retrieval of those instructions byprocessor 702. Data in the data caches may be copies of data in memory704 or storage 706 for instructions executing at processor 702 tooperate on; the results of previous instructions executed at processor702 for access by subsequent instructions executing at processor 702 orfor writing to memory 704 or storage 706; or other suitable data. Thedata caches may speed up read or write operations by processor 702. TheTLBs may speed up virtual-address translation for processor 702. Inparticular embodiments, processor 702 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 702 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 702may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 702. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 704 includes main memory for storinginstructions for processor 702 to execute or data for processor 702 tooperate on. As an example and not by way of limitation, computer system700 may load instructions from storage 706 or another source (such as,for example, another computer system 700) to memory 704. Processor 702may then load the instructions from memory 704 to an internal registeror internal cache. To execute the instructions, processor 702 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 702 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor702 may then write one or more of those results to memory 704. Inparticular embodiments, processor 702 executes only instructions in oneor more internal registers or internal caches or in memory 704 (asopposed to storage 706 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 704 (as opposedto storage 706 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 702 tomemory 704. Bus 712 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 702 and memory 704 and facilitateaccesses to memory 704 requested by processor 702. In particularembodiments, memory 704 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 704 may include one ormore memories 704, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 706 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 706may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage706 may include removable or non-removable (or fixed) media, whereappropriate. Storage 706 may be internal or external to computer system700, where appropriate. In particular embodiments, storage 706 isnon-volatile, solid-state memory. In particular embodiments, storage 706includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 706 taking any suitable physicalform. Storage 706 may include one or more storage control unitsfacilitating communication between processor 702 and storage 706, whereappropriate. Where appropriate, storage 706 may include one or morestorages 706. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 708 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 700 and one or more I/O devices. Computer system700 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 700. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 708 for them. Where appropriate, I/O interface 708 mayinclude one or more device or software drivers enabling processor 702 todrive one or more of these I/O devices. I/O interface 708 may includeone or more I/O interfaces 708, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 710 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 700 and one or more other computer systems 700 or one ormore networks. As an example and not by way of limitation, communicationinterface 710 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 710 for it. As an example and not by way of limitation,computer system 700 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 700 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 700 may include any suitable communication interface 710 for anyof these networks, where appropriate. Communication interface 710 mayinclude one or more communication interfaces 710, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 712 includes hardware, software, or bothcoupling components of computer system 700 to each other. As an exampleand not by way of limitation, bus 712 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 712may include one or more buses 712, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computingdevices: identifying, from a plurality of entities, a primary entity andone or more related entities for the primary entity based on one or morerelated-entity indexes associated with the primary entity, wherein eachrelated-entity index for the primary entity comprises identificationinformation of one or more related entities to the primary entity;identifying, for each identified related entity, one or more contentobjects associated with the related entity and the primary entity;extracting, for each identified related entity, structured data from theidentified content objects associated with the related entity, whereinthe structured data indicates a relationship between the primary entityand the related entity; generating, for each identified related entity,a snippet for the related entity based on an application of one or moresnippet templates to the structured data extracted from the identifiedcontent objects associated with the related entity, wherein each snippetdescribes the relationship between the primary entity and the relatedentity; and storing, for each identified related entity, the generatedsnippet in association with the identification information of therelated entity.
 2. The method of claim 1, further comprising: receivinga text query comprising one or more n-grams inputted by a first user. 3.The method of claim 1, wherein one or more of the related-entity indexesassociated with the primary entity are generated based at least in parton one or more online encyclopedic indexes.
 4. The method of claim 1,wherein one or more of the related-entity indexes associated with theprimary entity are generated based at least in part on social-graphinformation associated with the primary entity.
 5. The method of claim1, wherein one or more of the related-entity indexes associated with theprimary entity are generated based at least in part on a co-occurrenceof references to the primary entity with references to one or more ofthe related entities within one or more content objects associated withthe primary entity and one or more of the related entities.
 6. Themethod of claim 1, further comprising: generating one or more of therelated-entity indexes associated with the primary entity by:identifying one or more similar entities to the primary entity; andpopulating the one or more generated related-entity indexes associatedwith the primary entity with one or more related entities extracted fromone or more related-entity indexes associated with the identifiedsimilar entities.
 7. The method of claim 1, wherein the snippetgenerated for each of the identified related entities comprises a numberof characters less than or equal to a specified character limit.
 8. Themethod of claim 1, wherein, for one or more of the identified relatedentities, at least one of the identified content objects is published ona profile interface corresponding to the identified related entity. 9.The method of claim 1, wherein the extracted structured data comprisesone or more text strings, and wherein generating the snippet based onthe application of one or more snippet templates to the structured dataextracted from the identified content objects associated with therelated entity comprises one or more of: removing one or more phrasesfrom one or more of the text strings; adding one or more phrases to oneor more of the text strings; changing an order of one or more phraseswithin one or more of the text strings; or combining one or more of thetext strings.
 10. The method of claim 1, wherein generating the snippetbased on the application of one or more snippet templates to thestructured data extracted from the identified content objects associatedwith the related entity comprises analyzing the extracted structureddata using a natural-language processing algorithm.
 11. The method ofclaim 1, wherein generating the snippet is based on an application ofone or more pre-generated snippet templates to the structured dataextracted from the identified content objects associated with therelated entity.
 12. The method of claim 1, further comprising, for eachidentified related entity calculating a confidence score for thegenerated snippet based at least in part on one or more of: a measure ofreliability of the content objects associated with the related entity,or a measure of strength of a relationship between the primary entityand the related entity described by the snippet; and storing, for eachidentified related entity, the generated snippet having the greatestconfidence score in association with the identification information ofthe related entity.
 13. The method of claim 1, further comprising, forone or more of the identified related entities: calculating a score foreach of the identified content objects based at least in part on anumber of social signals associated with: the content object, and one ormore related entities associated with the content object; andgenerating, responsive to receiving a text query comprising one or moren-grams inputted by a first user, one or more search results comprisingreferences to one or more of the identified content objects,respectively, each having a calculated score greater than a thresholdscore.
 14. The method of claim 13, wherein the search results arepresented to the first user in a ranked order based at least in part onthe respective scores of the content objects referenced by the searchresults.
 15. The method of claim 13, wherein the social signalsassociated with the content object comprise one or more of: a number oflikes associated with the content object; a number of comments on thecontent object; a number of shares of the content object; or a change ina growth rate of an amount of social activities associated with thecontent object.
 16. The method of claim 13, wherein the social signalsassociated with a related entity comprise one or more of: a number offollowers for the related entity; a number of visits to a profileinterface corresponding to the related entity; or a number of likesassociated with the related entity.
 17. The method of claim 13, whereincalculating the score for each of the identified content objectscomprises: determining a first meaning of one of the n-grams of the textquery; determining a second meaning of an n-gram within the context ofthe content object; and calculating the score based at least in part ona level of matching between the first meaning and the second meaning.18. A system comprising: one or more processors; and a non-transitorymemory coupled to the processors comprising instructions executable bythe processors, the processors operable when executing the instructionsto: identify, from a plurality of entities, a primary entity and one ormore related entities for the primary entity based on one or morerelated-entity indexes associated with the primary entity, wherein eachrelated-entity index for the primary entity comprises identificationinformation of one or more related entities to the primary entity;identify, for each identified related entity, one or more contentobjects associated with the related entity and the primary entity;extract, for each identified related entity, structured data from theidentified content objects associated with the related entity, whereinthe structured data indicates a relationship between the primary entityand the related entity; generate, for each identified related entity, asnippet for the related entity based on an application of one or moresnippet templates to the structured data extracted from the identifiedcontent objects associated with the related entity, wherein each snippetdescribes the relationship between the primary entity and the relatedentity; and store, for each identified related entity, the generatedsnippet in association with the identification information of therelated entity.
 19. One or more computer-readable non-transitory storagemedia embodying software that is operable when executed to: identify,from a plurality of entities, a primary entity and one or more relatedentities for the primary entity based on one or more related-entityindexes associated with the primary entity, wherein each related-entityindex for the primary entity comprises identification information of oneor more related entities to the primary entity; identify, for eachidentified related entity, one or more content objects associated withthe related entity and the primary entity; extract, for each identifiedrelated entity, structured data from the identified content objectsassociated with the related entity, wherein the structured dataindicates a relationship between the primary entity and the relatedentity; generate, for each identified related entity, a snippet for therelated entity based on an application of one or more snippet templatesto the structured data extracted from the identified content objectsassociated with the related entity, wherein each snippet describes therelationship between the primary entity and the related entity; andstore, for each identified related entity, the generated snippet inassociation with the identification information of the related entity.